Towards AI

APRIL 3, 2024

3D methods although with an initial momentum (mainly due to the fact that we didn’t have sufficient resources and knowledge to efficiently train larger neural networks) suffer from lower quality and lower processing speeds. In this article, I will focus on machine learning methods. Image by Petrov I.

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Mlearning.ai

MAY 5, 2023

Introduction of Large Language Models One of the most influential LLMs is the GPT (Generative Pre-trained Transformer) model, which was first introduced by OpenAI in 2018. Knowledge of Neural Networks : LLMs are typically built using deep learning techniques, so you should have a good understanding of neural networks and how they work.

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Heartbeat

OCTOBER 18, 2023

By leveraging the power of neural networks, deep learning techniques breathe new life into recommendation algorithms, empowering them to handle complex data and surpass the limitations of their predecessors.

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Marktechpost

JULY 3, 2023

Predictive Analytics with AI: 3D Simulation (NCS) Since 2018, Neural Concept has been leveraging Deep Learning to provide a surrogate for CAE by learning to build its own predictive models with data mining of past CAE data. ML refers to the development of algorithms that enable computers to learn from data without explicit programming.

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Heartbeat

DECEMBER 8, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network.

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ODSC - Open Data Science

AUGUST 31, 2023

A Mongolian pharmaceutical company engaged in a pilot study in 2018 to detect fake drugs, an initiative with the potential to save hundreds of thousands of lives. The Role of AI in Counterfeit Detection Experts must bolster AI tools to be more proficient at being an anti-counterfeit technology than one to make illegal products.

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NVIDIA

DECEMBER 21, 2023

DigitalPath, based in Chico, California, has refined a convolutional neural network to spot wildfires. The mission is near and dear to DigitalPath employees, whose office sits not far from the town of Paradise, where California’s deadliest wildfire killed 85 people in 2018. We don’t want people to lose their lives.”

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. The first GPT model was introduced in 2018 by OpenAI. The other data challenge for healthcare customers are HIPAA compliance requirements.

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Mlearning.ai

MAY 8, 2023

A comprehensive step-by-step guide with data analysis, deep learning, and regularization techniques Introduction In this article, we will use different deep-learning TensorFlow neural networks to evaluate their performances in detecting whether cell nuclei mass from breast imaging is malignant or benign. This model has 2 hidden layers.

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ODSC - Open Data Science

JUNE 9, 2023

The 1970s introduced bell bottoms, case grammars, semantic networks, and conceptual dependency theory. In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). It uses a neural network to learn the vector representations of words from a large corpus of text.

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TheSequence

NOVEMBER 22, 2023

I co-authored my first AI-related paper in 2000 ( using neural networks to manage on-CPU hardware resources ). In 2018, when I moved into Azure, my team started building custom AI supercomputers at scale. Understanding biological neural networks is one current focus.

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Towards AI

JULY 20, 2023

An additional 2018 study found that each SLR takes nearly 1,200 total hours per project. This includes one paper from 2020 that conducted feature extraction using a denoising autoencoder alongside a deep neural network, and a flattened vector and support vector machines to evaluate study relevance. dollars apiece.

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ML @ CMU

JUNE 30, 2023

Object Goal Navigation We instantiate semantic navigation with the Object Goal navigation task [ Anderson 2018 ], where a robot starts in a completely unseen environment and is asked to find an instance of an object category, let’s say a toilet.

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Heartbeat

JANUARY 29, 2024

Memory-Augmented Neural Networks (MANNs) The path that led to Memory-Augmented Neural Networks started with conceptualizing the Neural Turing Machine (NTM). NTMs introduced the revolutionary concept of incorporating dynamic memory storage into neural networks. & Schulman, J.

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AI Impacts

FEBRUARY 1, 2023

arXiv preprint arXiv:1803.03453 (2018). More generally, large language models trained on internet text can extensively recount information about deep learning, neural networks, and the real-world contexts in which those networks are typically deployed; and can be fine-tuned to recount details about themselves specifically [OpenAI, 2022a].

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Dlabs.ai

AUGUST 20, 2020

Interestingly, one of the authors – Yoshua Bengio won the 2018 Turing Award (the Nobel Prize of computing) for his work in deep learning. Neural Networks and Deep Learning Author: Michael Nielsen If you want to learn about deep learning and neural networks, I highly recommend this resource.

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Dlabs.ai

SEPTEMBER 2, 2022

The leader in driverless technology confirmed the launch of its all-electric IONIQ 5-based robo-taxi: an autonomous vehicle built for driverless ride-hail operations on the Lyft network in Las Vegas. Both companies have been testing the solution since 2018, and the first driverless taxis will hit the streets of Las Vegas in early 2023.

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IBM Journey to AI blog

JUNE 23, 2023

The introduction of ChatGPT capabilities has generated a lot of interest in generative AI foundation models (these are pre-trained on unlabeled datasets and leverage self-supervised learning with the help of Large Language Models using a neural network ). The ROE ranges also varied by country, from –5% to +13% [1].

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Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. A combination of simulated and real-world data was used to train the system, enabling it to generalize to new objects and tasks.

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Unite.AI

AUGUST 3, 2023

Meanwhile, statistics provided the mathematical foundation to explore artificial neural networks , inspired by our biological brain. Our journey took a big leap when we joined the Techstars Mobility Accelerator in Detroit in 2018.

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Marek Rei

JANUARY 2, 2018

NAACL and COLING were notably missing from 2017, but we can look forward to both of them in 2018. In the middle is the topic of general machine learning, neural networks and adversarial learning. Looking forward to all the exciting research coming in 2018! That’s it for 2017.

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Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. Here’s a high-level overview of how it works.

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Dlabs.ai

AUGUST 24, 2019

The former aims to disrupt its field with an innovative solution, the latter is the innovation the world can’t get enough of – according to a Q4 2018 report from PwC and CB Insights, VC funding for AI startups topped a record $9.3 VC-funded startups and AI are a match made in heaven. billion (a 72% spike from last year ).

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Viso.ai

APRIL 25, 2024

Today’s boom in CV started with the implementation of deep learning models and convolutional neural networks (CNN). 2018) published their research titled MobileFaceNets. Lightweight computer vision models allow the users to deploy them on mobile and edge devices. They used less than 1 million parameters.

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Flipboard

AUGUST 25, 2023

Deep learning is a type of machine learning where artificial neural networks ( similar but not exactly like the neurons in our brain) allow a machine to learn and advance independent of human intervention.

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AWS Machine Learning Blog

APRIL 6, 2023

The point cloud-based neural network model is further trained using this data to learn the parameters of the product lifecycle curve (see the following figure). For example, in the 2019 WAPE value, we trained our model using sales data between 2011–2018 and predicted sales values for the next 12 months (2019 sale).

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PyImageSearch

JANUARY 2, 2023

Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 ( Figure 1 ). Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools.

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Unite.AI

OCTOBER 27, 2023

For example, multimodal generative models of neural networks can produce such images, literary and scientific texts that it is not always possible to distinguish whether they are created by a human or an artificial intelligence system. McLay, “Managing the rise of Artificial Intelligence,” 2018 Bertolini A. Sources: R.

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Flipboard

MARCH 13, 2023

Yes, large language models (LLMs) hallucinate , a concept popularized by Google AI researchers in 2018. That feedback is used to adjust the reward predictor neural network, and the updated reward predictor neural network is used to adjust the behavior of the AI model.

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The Stanford AI Lab Blog

OCTOBER 13, 2021

Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. 1112–1122, 2018. ↩ Yonatan Giefman and Ran El-Yaniv. SelectiveNet: A deep neural network with an integrated reject option. Selective classification for deep neural networks.

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Topbots

APRIL 11, 2023

We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers.

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Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

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ML Review

MARCH 5, 2019

Conducting exploratory search is difficult in standard IR systems as terminology might differ even in closely related fields (network analyses vs graph neural networks). In 2018, over 1000 papers have been released on ArXiv per month in the above areas. Every month except January.

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Mlearning.ai

MARCH 9, 2023

2018; Sitawarin et al., 2018; Papernot et al., 2018) investigated the vulnerability of deep learning models to adversarial attacks in medical image segmentation tasks, and proposed a method to improve their robustness. 2018; Pang et al., Neural Networks, 64, 59–63. Intriguing properties of neural networks.

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PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. Object detection is no different. 2015 ; He et al.,

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NVIDIA

DECEMBER 4, 2023

Since its 2018 launch, MLPerf , the industry-standard benchmark for AI, has provided numbers that detail the leading performance of NVIDIA GPUs on both AI training and inference. An AI model, also called a neural network, is essentially a mathematical lasagna, made from layer upon layer of linear algebra equations.

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Sebastian Ruder

AUGUST 15, 2021

Roberto Navigli discussing "hot" topics at ACL 2018 vs ACL 2021 during the opening session This serves as a useful reminder that the community is overfitting to a particular setting and that it may be worthwhile to look beyond the standard Transformer model (see my recent newsletter for some inspiration).

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